Types of Lab Experiences
There are many ways to classify research. We choose to do so by the day-to-day activities. A broad way to distinguish experiences is to categorize them into one of these two categories:
Wet Lab experiences - These can take the form of Chemical, Biological, Animal, or Clinical research.
Dry Lab experiences - These can take the form of Theoretical, Computational, Design, or Clinical research.
If you are searching for a lab, keep in mind that a given lab may overlap with more than one of these categories. There are many types of laboratory experiences, each of which has corresponding styles and attributes. We encourage students to consider the kind of experience that a particular lab may provide and how that aligns with their goals, interests, and work style (see the other posts in Getting Experience to learn more about this). Make a list of what you think your ideal lab would be like and update it as you learn.
Chemical
Research with chemicals can encompass a wide range of studies from analytical chemistry to materials to nanoparticles. It often involves designing, synthesizing, or obtaining some substance (i.e. chemical or material) and analyzing it’s properties. This may be for use in a particular application or purely for discovery-based purposes. In these types of labs, students spend time learning how to use specific equipment (microscopes, mass spectrometers, zetasizers, etc.) and analyzing samples/specimens with that equipment.
Example projects: Developing nanoparticles of a specific size with specific properties, Analyzing environmental samples, Developing methods to assess and characterize materials.
Biological
This type of lab involves creating and maintaining solutions that are used to create specific conditions for cell to live and change. This experience is hands on; you will pipette thousands of times and will have to know how to titrate chemicals to make exact solutions. Often you will create and use a variety of concentrations of chemicals to test how cells respond to them. This type of lab also often requires that you keep a cell line alive for long periods of time. This requires long consecutive hours of work and long wait times as cells need the right solutions at the right times. Techniques typically employed in wet labs include microscopy, PCR, sequencing, genetic manipulation, and drug treatments. The application of what you discover in a wet lab is relatively short as the medical field regularly updates their knowledge base and uses what is learned in wet labs to create treatments and run clinical trials.
Example projects: Testing the effect of drug treatments on cancer cell growth rates, Finding correlation between genetic markers and disease states, Identifying cellular pathway structures, Controlling stem cell development, Creating artificial organ substrates, Quantifying receptor binding constants
Animal
Animal labs deal with a small set of model organisms that have been studied for generations by scientists. They range from mice to primates, and, because animals are in similar in some ways to humans, the discoveries from animal labs can be moved forward into clinical trials (in humans). These labs give a hands-on experience and involve taking care of the animals, designing experiments to minimize harm, and relating the discoveries to the human body. Animal research is governed by the Institutional Animal Care and Use Committee (IACUC), and protocols must be approved before the work can begin. Much of the time spent in animal labs is on the setup of an experiment as the animal models can be costly and it is important that the experiments progress as desired. Workers in these labs must also be comfortable with sacrificing animals as they very rarely live beyond the study and most of the people in the lab will have to sacrifice some of them.
Example projects: Discovering stress induction and reduction techniques, Evaluating effects of learning enhancing supplements, Identifying genetic regulators through disabling of genes, Modelling addiction, Evaluating new surgical techniques, Growing organs in animal bodies.
Clinical
Clinical labs typically involve performing experiments on consenting human subjects or utilizing human specimens, including data, to draw conclusions. It can be either a wet or dry lab experience based on the specific work being done. There are many bureaucratic checkpoints to pass before human subjects research can proceed, such as gaining approval by the Institutional Review Board (IRB). All of the experimental protocols must be established before conducting any experiments, and subjects must be enrolled and consented before they can participate in the study. Clinical research often involves large teams, so it is important to understand your role in the research. The discoveries made in clinical work have immediate impact on human knowledge and health as the results directly pertain to humans.
Example projects: Evaluating psychological models, Testing the efficacy of cancer treatments, Identifying trends in responses to trauma, Testing perception models, Evaluating new pain reduction drug treatments, Performing new surgical techniques, Testing the effects of psychological drugs.
Theoretical
These lab experiences are not hands-on, instead they are heavy on reading and practice of mathematical techniques. You will spend most of your time doing long form proofs and condensing material to be easier to understand. These labs are often the most conceptually challenging and offer little confirmation about the results that you get. These labs also tend to be poorly funded, so graduate students in these labs usually teach the whole time they are in grad school. In a lab like this you often have the opportunity to work from anywhere. The results that you discover will take longer than any of the other categories to be applied to something that could benefit society, but also often have the largest impact.
Example projects: Identifying conditions during the big bang, Predicting state changes in new materials, Solving electricity and magnetism equations in esoteric geometries, Predicting bandwidth of computational systems, Simplifying equations in different parameter regimes.
Computational
This type of lab involves tremendous amounts of computer work. You will spend your time writing and debugging code, creating and improving simulations, and manipulating data formats to more easily be analyzed. You will need to be able to document your code so that others can understand it. An ‘experiment’ in this type of lab is different from others. You define a target for what you want code to do, create a data or input set that you know the desired output for, then make changes to the code until you are satisfied that is doing what you think it is doing. You may have the opportunity to collaborate with another lab that does experimental work or have access to a large data set to mine out of. You often have the opportunity to work remotely on computational projects and the implementation of what you learn can be very rapid or very long depending on what you research.
Example projects: Finite element simulation of objects or fluids, Simulation of cellular automation, Model development for disease states, Developing automation algorithms, Simulating particle interactions, Finding relationships in big data sets, Making algorithms to simulate a human brain.
Design
These labs focus on creating a solution to a known problem. The experiences in design labs are very hands-on and are typically found in engineering departments. In these labs you will spend time identifying constraints, prototyping solutions, validating your designs and working with others. The prototyping equipment is in the lab space so much of the work you do will be in lab, but there are many steps in the design process that are done on a computer, such as CAD, that can be done anywhere. The applications of creations from design labs are very rapid and are regularly transitioned to startup businesses or sold to a business for them to produce.
Example projects: Creating artificial limbs, Designing solar cells, Building a robot to carry heavy objects, Creating a vision system for drones, Designing airplane parts, Building detectors for scientific experiments, Building better 3D printers, Printing artificial cell substrates.
